In commercial fruit farming, managing the light distribution through canopies is important because the amount and distribution of solar energy that is harvested by each tree impacts the production of fruit quantity and quality. It is therefore an important characteristic to measure and ultimately to control with pruning. We present a solar-geometric model to estimate light interception in individual avocado (Persea americana Mill.) trees, that is designed to scale to whole-orchard scanning, ultimately to inform pruning decisions. The geometry of individual trees was measured using handheld mobile LiDAR and represented by point clouds. A discrete energy distribution model of the hemispherical sky was synthesised using public weather records. The light from each sky node was then ray traced, applying a radiation absorption model where rays pass the point cloud representation of the tree. The model was validated using ceptometer energy measurements at the canopy floor, and model parameters were optimised by analysing the error between modelled and measured energies. The model was shown to perform well qualitatively through visual comparison with tree shadows in photographs, and quantitatively well with R-2 = 0.854, suggesting it is suitable to use in the context of agricultural decision support systems, in future work.